Perelman School of Medicine at the University of Pennsylvania

Soccio Lab

  • Trichrome stain of fibrosis in Ppara-KO mice

    Trichrome stain of fibrosis in Ppara-KO mice (left: wild-type mouse on choline deficient diet; right: Ppar-alpha KO mouse)

  • Genome browser image of PPARalpha ChIP-seq.

    Genome browser view of PPARalpha binding sites in mouse liver, with genetic differences among three inbred strains.

  • We are located on the 12th Floor of the Smilow Center for Translational Research

Welcome to the Soccio Lab!

Welcome to the Soccio Lab!

Our vision is to characterize genetic variants in binding sites for metabolic transcription factors, ultimately for precision medicine.  Most natural genetic variation that drives phenotypic differences among individuals does not lie in coding regions of genes, but rather in regulatory “switches” that turn genes on or off.  Transcription factors (TFs) bind to regulatory elements and determine expression levels of target genes.  The Soccio Lab aims to discover mechanisms by which genetic variation in non-coding regulatory regions of the genome predisposes to metabolic diseases like diabetes, obesity, and dyslipidemia, and how this may be exploited for precision medicine.

The nuclear receptor TF PPARα, and the related TFs PPARγ and PPARδ, represent a prime opportunity to study functional regulatory variation.  Fibrate PPARα agonists are used in patients with dyslipidemia to lower triglycerides and raise HDL cholesterol, and other PPAR agonists mediate insulin sensitization and potential treatments for fatty liver disease.  We have deployed next generation sequencing methods to identify genome-wide those regulatory PPAR elements that are naturally polymorphic in mice or humans.  Candidates have emerged, in which metabolically relevant loci harbor natural regulatory variation that may determine gene expression, drug response, and disease risk.  Research like this on functional regulatory variation is an emerging frontier in human genetics, and will contribute fundamentally to understanding of individualized disease risk and treatment.